Overview

Brought to you by YData

Dataset statistics

Number of variables8
Number of observations992
Missing cells60
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory149.2 KiB
Average record size in memory154.0 B

Variable types

Numeric6
Categorical1
Text1

Alerts

Height is highly overall correlated with Sex and 1 other fieldsHigh correlation
Sex is highly overall correlated with HeightHigh correlation
Weight is highly overall correlated with HeightHigh correlation
Humidity has 30 (3.0%) missing values Missing
Temperature has 30 (3.0%) missing values Missing
ID_test has unique values Unique

Reproduction

Analysis started2025-04-13 15:05:33.142609
Analysis finished2025-04-13 15:06:52.142096
Duration1 minute and 19 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct348
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.979133
Minimum10.8
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-04-13T11:06:52.340294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10.8
5-th percentile15.7
Q121.1
median27.1
Q336.325
95-th percentile47
Maximum63
Range52.2
Interquartile range (IQR)15.225

Descriptive statistics

Standard deviation10.076653
Coefficient of variation (CV)0.34772098
Kurtosis-0.24545327
Mean28.979133
Median Absolute Deviation (MAD)7.65
Skewness0.59914948
Sum28747.3
Variance101.53893
MonotonicityIncreasing
2025-04-13T11:06:52.650469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.1 11
 
1.1%
26.9 10
 
1.0%
25.9 9
 
0.9%
31.9 8
 
0.8%
39.1 8
 
0.8%
16.3 8
 
0.8%
22.7 8
 
0.8%
22.4 8
 
0.8%
16.2 7
 
0.7%
26.4 7
 
0.7%
Other values (338) 908
91.5%
ValueCountFrequency (%)
10.8 1
 
0.1%
11.8 1
 
0.1%
12.2 1
 
0.1%
13.2 1
 
0.1%
13.7 1
 
0.1%
13.8 1
 
0.1%
14 1
 
0.1%
14.1 4
0.4%
14.2 5
0.5%
14.3 5
0.5%
ValueCountFrequency (%)
63 1
0.1%
61.6 1
0.1%
61.3 1
0.1%
59.7 1
0.1%
59.1 1
0.1%
58.7 1
0.1%
58.5 1
0.1%
57.6 1
0.1%
55.5 1
0.1%
55.4 2
0.2%

Weight
Real number (ℝ)

High correlation 

Distinct264
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.383367
Minimum41
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-04-13T11:06:52.951888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile54.165
Q166
median73
Q380.225
95-th percentile93.135
Maximum135
Range94
Interquartile range (IQR)14.225

Descriptive statistics

Standard deviation12.005361
Coefficient of variation (CV)0.16359785
Kurtosis1.4049884
Mean73.383367
Median Absolute Deviation (MAD)7
Skewness0.49731123
Sum72796.3
Variance144.12869
MonotonicityNot monotonic
2025-04-13T11:06:53.248850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 41
 
4.1%
74 30
 
3.0%
67 29
 
2.9%
72 27
 
2.7%
76 27
 
2.7%
71 26
 
2.6%
70 26
 
2.6%
75 25
 
2.5%
66 24
 
2.4%
78 24
 
2.4%
Other values (254) 713
71.9%
ValueCountFrequency (%)
41 1
 
0.1%
41.9 1
 
0.1%
42 1
 
0.1%
46 3
0.3%
46.6 1
 
0.1%
47.2 1
 
0.1%
48 1
 
0.1%
48.6 1
 
0.1%
48.8 1
 
0.1%
49 2
0.2%
ValueCountFrequency (%)
135 1
0.1%
127 1
0.1%
122 1
0.1%
116 1
0.1%
113 1
0.1%
112.2 1
0.1%
110 2
0.2%
109 1
0.1%
108.3 1
0.1%
108 2
0.2%

Height
Real number (ℝ)

High correlation 

Distinct182
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.91351
Minimum150
Maximum203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-04-13T11:06:53.572525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile161.775
Q1170
median175
Q3180
95-th percentile187
Maximum203
Range53
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.9500267
Coefficient of variation (CV)0.045451188
Kurtosis0.3030682
Mean174.91351
Median Absolute Deviation (MAD)5
Skewness-0.04549302
Sum173514.2
Variance63.202925
MonotonicityNot monotonic
2025-04-13T11:06:54.161441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178 47
 
4.7%
174 44
 
4.4%
176 42
 
4.2%
172 40
 
4.0%
173 39
 
3.9%
179 38
 
3.8%
171 37
 
3.7%
170 35
 
3.5%
175 34
 
3.4%
180 34
 
3.4%
Other values (172) 602
60.7%
ValueCountFrequency (%)
150 1
 
0.1%
151 1
 
0.1%
152 2
 
0.2%
153 1
 
0.1%
154 3
0.3%
155 3
0.3%
156 4
0.4%
157 5
0.5%
158 5
0.5%
159 4
0.4%
ValueCountFrequency (%)
203 1
 
0.1%
201 1
 
0.1%
199 1
 
0.1%
197.5 1
 
0.1%
197.4 1
 
0.1%
197 4
0.4%
195 1
 
0.1%
194.2 1
 
0.1%
193 5
0.5%
192 2
 
0.2%

Humidity
Real number (ℝ)

Missing 

Distinct52
Distinct (%)5.4%
Missing30
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean48.211435
Minimum23.7
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-04-13T11:06:54.520299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum23.7
5-th percentile35
Q142
median47
Q354
95-th percentile64
Maximum69
Range45.3
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.560991
Coefficient of variation (CV)0.17757179
Kurtosis-0.58075794
Mean48.211435
Median Absolute Deviation (MAD)6
Skewness0.32211548
Sum46379.4
Variance73.290566
MonotonicityNot monotonic
2025-04-13T11:06:54.820684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 61
 
6.1%
44 49
 
4.9%
43 47
 
4.7%
42 47
 
4.7%
45 44
 
4.4%
52 43
 
4.3%
39 42
 
4.2%
41 39
 
3.9%
40 34
 
3.4%
49 34
 
3.4%
Other values (42) 522
52.6%
ValueCountFrequency (%)
23.7 1
 
0.1%
28 1
 
0.1%
31 3
 
0.3%
32 6
 
0.6%
32.2 2
 
0.2%
33 6
 
0.6%
34 12
1.2%
35 22
2.2%
36 10
1.0%
37 13
1.3%
ValueCountFrequency (%)
69 2
 
0.2%
68 3
 
0.3%
67 14
1.4%
66 14
1.4%
65.9 1
 
0.1%
65 9
0.9%
64 9
0.9%
63 8
0.8%
62 14
1.4%
61 16
1.6%

Temperature
Real number (ℝ)

Missing 

Distinct122
Distinct (%)12.7%
Missing30
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean22.818565
Minimum15
Maximum32.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-04-13T11:06:55.104226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile18.3
Q120.8
median22.9
Q324.4
95-th percentile27.395
Maximum32.3
Range17.3
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation2.7840663
Coefficient of variation (CV)0.12200882
Kurtosis0.47360183
Mean22.818565
Median Absolute Deviation (MAD)1.8
Skewness0.33509091
Sum21951.46
Variance7.751025
MonotonicityNot monotonic
2025-04-13T11:06:55.471655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.8 34
 
3.4%
22 26
 
2.6%
21 22
 
2.2%
23.5 22
 
2.2%
20 21
 
2.1%
22.3 20
 
2.0%
23.3 19
 
1.9%
25 19
 
1.9%
24.3 19
 
1.9%
20.3 19
 
1.9%
Other values (112) 741
74.7%
(Missing) 30
 
3.0%
ValueCountFrequency (%)
15 3
0.3%
16.5 2
0.2%
16.7 1
 
0.1%
16.8 2
0.2%
16.9 2
0.2%
17 3
0.3%
17.1 3
0.3%
17.2 1
 
0.1%
17.3 3
0.3%
17.4 1
 
0.1%
ValueCountFrequency (%)
32.3 1
 
0.1%
32 3
0.3%
30.6 5
0.5%
30.5 1
 
0.1%
30.1 3
0.3%
29.7 1
 
0.1%
29.5 5
0.5%
29.4 7
0.7%
29.3 6
0.6%
29.1 4
0.4%

Sex
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size48.6 KiB
0
843 
1
149 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters992
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 843
85.0%
1 149
 
15.0%

Length

2025-04-13T11:06:55.784048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-13T11:06:56.042084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 843
85.0%
1 149
 
15.0%

Most occurring characters

ValueCountFrequency (%)
0 843
85.0%
1 149
 
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 992
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 843
85.0%
1 149
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 992
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 843
85.0%
1 149
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 992
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 843
85.0%
1 149
 
15.0%

ID
Real number (ℝ)

Distinct857
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean424.89012
Minimum1
Maximum857
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-04-13T11:06:56.296730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45
Q1214.75
median428.5
Q3626.25
95-th percentile813.45
Maximum857
Range856
Interquartile range (IQR)411.5

Descriptive statistics

Standard deviation243.83248
Coefficient of variation (CV)0.57387185
Kurtosis-1.1422165
Mean424.89012
Median Absolute Deviation (MAD)206
Skewness0.0083793149
Sum421491
Variance59454.278
MonotonicityNot monotonic
2025-04-13T11:06:56.669063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
506 5
 
0.5%
492 4
 
0.4%
511 3
 
0.3%
552 3
 
0.3%
553 3
 
0.3%
351 3
 
0.3%
417 3
 
0.3%
58 3
 
0.3%
499 3
 
0.3%
99 3
 
0.3%
Other values (847) 959
96.7%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 2
0.2%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
857 1
 
0.1%
856 3
0.3%
855 2
0.2%
854 1
 
0.1%
853 1
 
0.1%
852 1
 
0.1%
851 1
 
0.1%
850 1
 
0.1%
849 1
 
0.1%
848 1
 
0.1%

ID_test
Text

Unique 

Distinct992
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
2025-04-13T11:06:57.827778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.9747984
Min length3

Characters and Unicode

Total characters4935
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique992 ?
Unique (%)100.0%

Sample

1st row543_1
2nd row11_1
3rd row829_1
4th row284_1
5th row341_1
ValueCountFrequency (%)
543_1 1
 
0.1%
344_1 1
 
0.1%
336_5 1
 
0.1%
829_1 1
 
0.1%
284_1 1
 
0.1%
341_1 1
 
0.1%
341_2 1
 
0.1%
343_1 1
 
0.1%
330_1 1
 
0.1%
338_1 1
 
0.1%
Other values (982) 982
99.0%
2025-04-13T11:06:59.354052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1228
24.9%
_ 992
20.1%
5 368
 
7.5%
3 360
 
7.3%
4 348
 
7.1%
2 337
 
6.8%
6 320
 
6.5%
7 306
 
6.2%
8 266
 
5.4%
9 209
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4935
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1228
24.9%
_ 992
20.1%
5 368
 
7.5%
3 360
 
7.3%
4 348
 
7.1%
2 337
 
6.8%
6 320
 
6.5%
7 306
 
6.2%
8 266
 
5.4%
9 209
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4935
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1228
24.9%
_ 992
20.1%
5 368
 
7.5%
3 360
 
7.3%
4 348
 
7.1%
2 337
 
6.8%
6 320
 
6.5%
7 306
 
6.2%
8 266
 
5.4%
9 209
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4935
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1228
24.9%
_ 992
20.1%
5 368
 
7.5%
3 360
 
7.3%
4 348
 
7.1%
2 337
 
6.8%
6 320
 
6.5%
7 306
 
6.2%
8 266
 
5.4%
9 209
 
4.2%

Interactions

2025-04-13T11:06:20.195511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:05:33.671709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:05:43.638337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:05:52.922468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:06:04.008740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:06:12.338347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:06:24.425909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:05:33.975572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:05:43.860092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:05:53.279665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:06:04.278661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:06:12.605001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:06:29.552139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:05:34.200070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:05:44.034410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:05:53.694618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:06:04.483990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:06:12.827565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:06:33.278131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:05:34.438220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:05:44.255806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:05:53.882921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:06:04.710547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:06:13.042562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:06:37.129294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:05:34.674479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:05:44.462123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:05:54.086912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:06:04.914485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:06:13.299761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:06:41.189528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:05:34.974906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:05:44.738103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:05:54.340718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:06:05.266312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-13T11:06:13.606570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-04-13T11:06:59.608910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AgeHeightHumidityIDSexTemperatureWeight
Age1.000-0.003-0.0420.0730.125-0.1330.196
Height-0.0031.000-0.015-0.0100.5290.0310.706
Humidity-0.042-0.0151.000-0.1970.039-0.1100.040
ID0.073-0.010-0.1971.0000.369-0.336-0.049
Sex0.1250.5290.0390.3691.0000.0540.445
Temperature-0.1330.031-0.110-0.3360.0541.000-0.020
Weight0.1960.7060.040-0.0490.445-0.0201.000

Missing values

2025-04-13T11:06:51.469587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-13T11:06:51.781319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-13T11:06:52.017140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AgeWeightHeightHumidityTemperatureSexIDID_test
010.848.8163.039.020.71543543_1
111.841.0150.041.022.311111_1
212.246.0160.037.021.50829829_1
313.271.0190.049.023.81284284_1
413.753.8169.740.025.30341341_1
513.853.4171.042.024.40341341_2
614.046.0160.040.025.30343343_1
714.150.0168.942.024.20330330_1
814.147.2160.240.025.20338338_1
914.149.7160.140.025.80339339_1
AgeWeightHeightHumidityTemperatureSexIDID_test
98255.478.0175.651.023.40597597_1
98355.561.5168.844.021.00598598_1
98457.667.0169.047.018.40389389_1
98558.564.0157.035.021.51755755_1
98658.766.0171.338.015.00856856_1
98759.164.7172.038.024.40856856_2
98859.765.2172.051.016.80856856_3
98961.3102.0185.056.020.50390390_1
99061.674.0169.046.023.90596596_1
99163.083.5171.548.022.20296296_1